A new comprehensive surface temperature data set for India is used to document changes in Indian temperature over seven decades, in order to examine the patterns and possible effects of global warming. The data set is subdivided into pre-monsoon, monsoon, and post-monsoon categories in order to study the temperature patterns in each of these periods.

An attribution study has been performed to investigate the degree to which the unusually cold European winter of 2009/10 was modified by anthropogenic climate change. Two different methods have been included for the attribution: one based on large HadGEM3-A ensembles and one based on a statistical surrogate method. Both methods are evaluated by comparing simulated winter temperature means, trends, standard deviations, skewness, return periods, and 5% quantiles with observations.

Wintertime windstorms associated with low-pressure systems from the North Atlantic Ocean are the costliest natural hazard for Europe. These storms are associated with large pressure gradients and high background winds, but the most destructive gusts are often confined to relatively small areas within the low-pressure systems.

Deep water convection (DC) in winter is one of the major processes driving open-ocean primary productivity in the Northwestern Mediterranean Sea. DC is highly variable in time, depending on the specific conditions (stratification, circulation and ocean-atmosphere interactions) of each specific winter. This variability also drives the interannual oscillations of open-ocean primary productivity in this important region for many commercially-important fish species.

A bibliometric approach is used in this study for the assessment of greenhouse gas (GHG) research trends on a global scale. The relevant literature published from 2000 to 2014 in journals of all subject categories of the Science Citation Index Expanded from the Web of Science Core Collection databases has been used. The strings ‘greenhouse gas*’ or ‘green house gas*’ are used for retrieving data.

Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Our study is based on an extensive gridded dataset of winter salinity in the upper 50 m layer of the Arctic Ocean for the periods 1950–1993 and 2007–2012, obtained from ~20 000 profiles. We investigate the interannual variability of the salinity fields, identify predominant patterns of anomalous behavior and leading modes of variability, and develop a statistical model for the prediction of surface-layer salinity.

Extreme positive Indian Ocean Dipole (pIOD) affects weather, agriculture, ecosystems, and public health worldwide, particularly when exacerbated by an extreme El Niño. The Paris Agreement aims to limit warming below 2 °C and ideally below 1.5 °C in global mean temperature (GMT), but how extreme pIOD will respond to this target is unclear.

The Atlantic meridional overturning circulation (AMOC)—a system of ocean currents in the North Atlantic—has a major impact on climate, yet its evolution during the industrial era is poorly known owing to a lack of direct current measurements. Here we provide evidence for a weakening of the AMOC by about 3 ± 1 sverdrups (around 15 per cent) since the mid-twentieth century.

Explaining the ~5-million-year delay in marine biotic recovery following the latest Permian mass extinction, the largest biotic crisis of the Phanerozoic, is a fundamental challenge for both geological and biological sciences. Ocean redox perturbations may have played a critical role in this delayed recovery. However, the lack of quantitative constraints on the details of Early Triassic oceanic anoxia (for example, time, duration, and extent) leaves the links between oceanic conditions and the delayed biotic recovery ambiguous.

Event attribution in the context of climate change seeks to understand the role of anthropogenic greenhouse gas emissions on extreme weather events, either specific events or classes of events. A common approach to event attribution uses climate model output under factual (real-world) and counterfactual (world that might have been without anthropogenic greenhouse gas emissions) scenarios to estimate the probabilities of the event of interest under the two scenarios.

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